ABSTRACT
With the widespread use of social media platforms within our modern society, these platforms have become a popular medium for disseminating news across the globe. While some of these platforms are considered reliable sources for sharing news, others publicize the information without much validation. The transmission of fake news on social media impacts people's behavior and negatively influences people's decisions. During the COVID-19 outbreak, it was more evident than ever. This has led to a demand for conducting research studies to explore sophisticated approaches to assess the integrity of news worldwide. The main objective of this research paper was to outline our proposed experimental methodology to detect and access fake news using Data Mining and Natural Language Processing. The presented research effort provides a method to verify the authenticity of the news disseminated in social networks by dividing the process into four significant stages: news aggregation, publication collection, data analysis, and matching results. © 2022 ACM.